| 1. |
- Ciumas, C., et al.
(författare)
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White matter development in children with benign childhood epilepsy with centro-temporal spikes
- 2014
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Ingår i: Brain. - : Oxford University Press (OUP). - 0006-8950 .- 1460-2156. ; 137:4, s. 1095-1106
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Tidskriftsartikel (refereegranskat)abstract
- Benign childhood epilepsy with centro-temporal spikes (BCECTS) is associated with cognitive disturbances thought to reflect interference between the epileptic focus and brain development. Using diffusion tensor imaging, Ciumas et al. demonstrate abnormal maturation of white matter at the epileptic focus, which correlates with duration of epilepsy and cognitive performance.Benign childhood epilepsy with centro-temporal spikes (BCECTS) is a unique form of non-lesional age-dependent epilepsy with rare seizures, focal electroencepalographic abnormalities affecting the same well delineated cortical region in most patients, and frequent mild to moderate cognitive dysfunctions. In this condition, it is hypothesized that interictal electroencepalographic discharges might interfere with local brain maturation, resulting in altered cognition. Diffusion tensor imaging allows testing of this hypothesis by investigating the white matter microstructure, and has previously proved sensitive to epilepsy-related alterations of fractional anisotropy and diffusivity. However, no diffusion tensor imaging study has yet been performed with a focus on BCECTS. We investigated 25 children suffering from BCECTS and 25 age-matched control subjects using diffusion tensor imaging, 3D-T-1 magnetic resonance imaging, and a battery of neuropsychological tests including Conner's scale and Wechsler Intelligence Scale for Children (fourth revision). Electroencephalography was also performed in all patients within 2 months of the magnetic resonance imaging assessment. Parametric maps of fractional anisotropy, mean-, radial-, and axial diffusivity were extracted from diffusion tensor imaging data. Patients were compared with control subjects using voxel-based statistics and family-wise error correction for multiple comparisons. Each patient was also compared to control subjects. Fractional anisotropy and diffusivity images were correlated to neuropsychological and clinical variables. Group analysis showed significantly reduced fractional anisotropy and increased diffusivity in patients compared with control subjects, predominantly over the left pre- and postcentral gyri and ipsilateral to the electroencephalographic focus. At the individual level, regions of significant differences were observed in 10 patients (40%) for anisotropy (eight reduced fractional anisotropy, one increased fractional anisotropy, one both), and 17 (56%) for diffusivity (13 increased, one reduced, three both). There were significant negative correlations between fractional anisotropy maps and duration of epilepsy in the precentral gyri, bilaterally, and in the left postcentral gyrus. Accordingly, 9 of 12 patients (75%) with duration of epilepsy > 12 months showed significantly reduced fractional anisotropy versus none of the 13 patients with duration of epilepsy 12 months. Diffusivity maps positively correlated with duration of epilepsy in the cuneus. Children with BCECTS demonstrate alterations in the microstructure of the white matter, undetectable with conventional magnetic resonance imaging, predominating over the regions displaying chronic interictal epileptiform discharges. The association observed between diffusion tensor imaging changes, duration of epilepsy and cognitive performance appears compatible with the hypothesis that interictal epileptic activity alters brain maturation, which could in turn lead to cognitive dysfunction. However, such cross-sectional association does not demonstrate causality, and other hitherto unidentified factors could represent the common cause to part or all of the observed findings.
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| 2. |
- Ledig, C., et al.
(författare)
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Robust whole-brain segmentation: Application to traumatic brain injury
- 2015
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Ingår i: Medical Image Analysis. - : Elsevier BV. - 1361-8415. ; 21:1, s. 40-58
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Tidskriftsartikel (refereegranskat)abstract
- We propose a framework for the robust and fully-automatic segmentation of magnetic resonance (MR) brain images called "Multi-Atlas-Label Propagation with Expectation-Maximisation based refinement" (MALP-EM). The presented approach is based on a robust registration approach (MAPER), highly performant label fusion (joint label fusion) and intensity-based label refinement using EM. We further adapt this framework to be applicable for the segmentation of brain images with gross changes in anatomy. We propose to account for consistent registration errors by relaxing anatomical priors obtained by multi-atlas propagation and a weighting scheme to locally combine anatomical atlas priors and intensity-refined posterior probabilities. The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge. In this context we show that MALP-EM is competitive for the segmentation of MR brain scans of healthy adults when compared to state-of-the-art automatic labelling techniques. To demonstrate the versatility of the proposed approach, we employed MALP-EM to segment 125 MR brain images into 134 regions from subjects who had sustained traumatic brain injury (TBI). We employ a protocol to assess segmentation quality if no manual reference labels are available. Based on this protocol, three independent, blinded raters confirmed on 13 MR brain scans with pathology that MALP-EM is superior to established label fusion techniques. We visually confirm the robustness of our segmentation approach on the full cohort and investigate the potential of derived symmetry-based imaging biomarkers that correlate with and predict clinically relevant variables in TBI such as the Marshall Classification (MC) or Glasgow Outcome Score (GOS). Specifically, we show that we are able to stratify TBI patients with favourable outcomes from non-favourable outcomes with 64.7% accuracy using acute-phase MR images and 66.8% accuracy using follow-up MR images. Furthermore, we are able to differentiate subjects with the presence of a mass lesion or midline shift from those with diffuse brain injury with 76.0% accuracy. The thalamus, putamen, pallidum and hippocampus are particularly affected. Their involvement predicts TBI disease progression.
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| 3. |
- Prange, S., et al.
(författare)
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Early limbic microstructural alterations in apathy and depression in de novo Parkinson's disease
- 2019
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Ingår i: Movement Disorders. - : Wiley. - 0885-3185 .- 1531-8257. ; 34:11, s. 1644-1654
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Tidskriftsartikel (refereegranskat)abstract
- Background Whether structural alterations underpin apathy and depression in de novo parkinsonian patients is unknown. The objectives of this study were to investigate whether apathy and depression in de novo parkinsonian patients are related to structural alterations and how structural abnormalities relate to serotonergic or dopaminergic dysfunction. Methods We compared the morphological and microstructural architecture in gray matter using voxel-based morphometry and diffusion tensor imaging coupled with white matter tract-based spatial statistics in a multimodal imaging case-control study enrolling 14 apathetic and 13 nonapathetic patients with de novo Parkinson's disease and 15 age-matched healthy controls, paired with PET imaging of the presynaptic dopaminergic and serotonergic systems. Results De novo parkinsonian patients with apathy had bilateral microstructural alterations in the medial corticostriatal limbic system, exhibiting decreased fractional anisotropy and increased mean diffusivity in the anterior striatum and pregenual anterior cingulate cortex in conjunction with serotonergic dysfunction. Furthermore, microstructural alterations extended to the medial frontal cortex, the subgenual anterior cingulate cortex and subcallosal gyrus, the medial thalamus, and the caudal midbrain, suggesting disruption of long-range nondopaminergic projections originating in the brainstem, in addition to microstructural alterations in callosal interhemispheric connections and frontostriatal association tracts early in the disease course. In addition, microstructural abnormalities related to depressive symptoms in apathetic and nonapathetic patients revealed a distinct, mainly right-sided limbic subnetwork involving limbic and frontal association tracts. Conclusions Early limbic microstructural alterations specifically related to apathy and depression emphasize the role of early disruption of ascending nondopaminergic projections and related corticocortical and corticosubcortical networks which underpin the variable expression of nonmotor and neuropsychiatric symptoms in Parkinson's disease. (c) 2019 International Parkinson and Movement Disorder Society
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| 4. |
- Rizzo, G., et al.
(författare)
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The predictive power of brain mRNA mappings for in vivo protein density: a positron emission tomography correlation study
- 2014
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Ingår i: Journal of Cerebral Blood Flow and Metabolism. - : SAGE Publications. - 0271-678X .- 1559-7016. ; 34:5, s. 827-835
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Tidskriftsartikel (refereegranskat)abstract
- Substantial efforts are being spent on postmortem mRNA transcription mapping on the assumption that in vivo protein distribution can be predicted from such data. We tested this assumption by comparing mRNA transcription maps from the Allen Human Brain Atlas with reference protein concentration maps acquired with positron emission tomography (PET) in two representative systems of neurotransmission (opioid and serotoninergic). We found a tight correlation between mRNA expression and specific binding with 5-HT1A receptors measured with PET, but for opioid receptors, the correlation was weak. The discrepancy can be explained by differences in expression regulation between the two systems: transcriptional mechanisms dominate the regulation in the serotoninergic system, whereas in the opioid system proteins are further modulated after transcription. We conclude that mRNA information can be exploited for systems where translational mechanisms predominantly regulate expression. Where posttranscriptional mechanisms are important, mRNA data have to be interpreted with caution. The methodology developed here can be used for probing assumptions about the relationship of mRNA and protein in multiple neurotransmission systems.
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| 5. |
- Heckemann, Rolf A., et al.
(författare)
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Brain Extraction Using Label Propagation and Group Agreement: Pincram
- 2015
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Ingår i: Plos One. - : Public Library of Science (PLoS). - 1932-6203. ; 10:7
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Tidskriftsartikel (refereegranskat)abstract
- Accurately delineating the brain on magnetic resonance (MR) images of the head is a prerequisite for many neuroimaging methods. Most existing methods exhibit disadvantages in that they are laborious, yield inconsistent results, and/or require training data to closely match the data to be processed. Here, we present pincram, an automatic, versatile method for accurately labelling the adult brain on T1-weighted 3D MR head images. The method uses an iterative refinement approach to propagate labels from multiple atlases to a given target image using image registration. At each refinement level, a consensus label is generated. At the subsequent level, the search for the brain boundary is constrained to the neighbourhood of the boundary of this consensus label. The method achieves high accuracy (Jaccard coefficient > 0.95 on typical data, corresponding to a Dice similarity coefficient of > 0.97) and performs better than many state-of-the-art methods as evidenced by independent evaluation on the Segmentation Validation Engine. Via a novel self-monitoring feature, the program generates the " success index," a scalar metadatum indicative of the accuracy of the output label. Pincram is available as open source software.
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| 6. |
- Ledig, C., et al.
(författare)
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Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database
- 2018
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Magnetic resonance (MR) imaging is a powerful technique for non-invasive in-vivo imaging of the human brain. We employed a recently validated method for robust cross-sectional and longitudinal segmentation of MR brain images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Specifically, we segmented 5074 MR brain images into 138 anatomical regions and extracted time-point specific structural volumes and volume change during follow-up intervals of 12 or 24 months. We assessed the extracted biomarkers by determining their power to predict diagnostic classification and by comparing atrophy rates to published meta-studies. The approach enables comprehensive analysis of structural changes within the whole brain. The discriminative power of individual biomarkers (volumes/atrophy rates) is on par with results published by other groups. We publish all quality-checked brain masks, structural segmentations, and extracted biomarkers along with this article. We further share the methodology for brain extraction (pincram) and segmentation (MALPEM, MALPEM4D) as open source projects with the community. The identified biomarkers hold great potential for deeper analysis, and the validated methodology can readily be applied to other imaging cohorts.
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| 7. |
- McGinnity, Colm J, et al.
(författare)
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Αlpha 5 subunit-containing GABAA receptors in temporal lobe epilepsy with normal MRI.
- 2021
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Ingår i: Brain communications. - : Oxford University Press (OUP). - 2632-1297. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- GABAA receptors containing the α5 subunit mediate tonic inhibition and are widely expressed in the limbic system. In animals, activation of α5-containing receptors impairs hippocampus-dependent memory. Temporal lobe epilepsy is associated with memory impairments related to neuron loss and other changes. The less selective PET ligand [11C]flumazenil has revealed reductions in GABAA receptors. The hypothesis that α5 subunit receptor alterations are present in temporal lobe epilepsy and could contribute to impaired memory is untested. We compared α5 subunit availability between individuals with temporal lobe epilepsy and normal structural MRI ('MRI-negative') and healthy controls, and interrogated the relationship between α5 subunit availability and episodic memory performance, in a cross-sectional study. Twenty-three healthy male controls (median ± interquartile age 49±13years) and 11 individuals with MRI-negative temporal lobe epilepsy (seven males; 40±8) had a 90-min PET scan after bolus injection of [11C]Ro15-4513, with arterial blood sampling and metabolite correction. All those with epilepsy and six controls completed the Adult Memory and Information Processing Battery on the scanning day. 'Bandpass' exponential spectral analyses were used to calculate volumes of distribution separately for the fast component [VF; dominated by signal from α1 (α2, α3)-containing receptors] and the slow component (VS; dominated by signal from α5-containing receptors). We made voxel-by-voxel comparisons between: the epilepsy and control groups; each individual case versus the controls. We obtained parametric maps of VF and VS measures from a single bolus injection of [11C]Ro15-4513. The epilepsy group had higher VS in anterior medial and lateral aspects of the temporal lobes, the anterior cingulate gyri, the presumed area tempestas (piriform cortex) and the insulae, in addition to increases of ∼24% and ∼26% in the ipsilateral and contralateral hippocampal areas (P<0.004). This was associated with reduced VF:VS ratios within the same areas (P<0.009). Comparisons of VS for each individual with epilepsy versus controls did not consistently lateralize the epileptogenic lobe. Memory scores were significantly lower in the epilepsy group than in controls (mean ± standard deviation -0.4±1.0 versus 0.7±0.3; P=0.02). In individuals with epilepsy, hippocampal VS did not correlate with memory performance on the Adult Memory and Information Processing Battery. They had reduced VF in the hippocampal area, which was significant ipsilaterally (P=0.03), as expected from [11C]flumazenil studies. We found increased tonic inhibitory neurotransmission in our cohort of MRI-negative temporal lobe epilepsy who also had co-morbid memory impairments. Our findings are consistent with a subunit shift from α1/2/3 to α5 in MRI-negative temporal lobe epilepsy.
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| 8. |
- Scott, G., et al.
(författare)
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Amyloid pathology and axonal injury after brain trauma
- 2016
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Ingår i: Neurology. - : Ovid Technologies (Wolters Kluwer Health). - 0028-3878 .- 1526-632X. ; 86:9, s. 821-828
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Tidskriftsartikel (refereegranskat)abstract
- Objective:To image -amyloid (A) plaque burden in long-term survivors of traumatic brain injury (TBI), test whether traumatic axonal injury and A are correlated, and compare the spatial distribution of A to Alzheimer disease (AD).Methods:Patients 11 months to 17 years after moderate-severe TBI underwent C-11-Pittsburgh compound B (C-11-PiB)-PET, structural and diffusion MRI, and neuropsychological examination. Healthy aged controls and patients with AD underwent PET and structural MRI. Binding potential (BPND) images of C-11-PiB, which index A plaque density, were computed using an automatic reference region extraction procedure. Voxelwise and regional differences in BPND were assessed. In TBI, a measure of white matter integrity, fractional anisotropy, was estimated and correlated with C-11-PiB BP(ND.)Results:Twenty-eight participants (9 with TBI, 9 controls, 10 with AD) were assessed. Increased C-11-PiB BPND was found in TBI vs controls in the posterior cingulate cortex and cerebellum. Binding in the posterior cingulate cortex increased with decreasing fractional anisotropy of associated white matter tracts and increased with time since injury. Compared to AD, binding after TBI was lower in neocortical regions but increased in the cerebellum.Conclusions:Increased A burden was observed in TBI. The distribution overlaps with, but is distinct from, that of AD. This suggests a mechanistic link between TBI and the development of neuropathologic features of dementia, which may relate to axonal damage produced by the injury.
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| 9. |
- Wild, H. M., et al.
(författare)
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Gyri of the human parietal lobe: Volumes, spatial extents, automatic labelling, and probabilistic atlases
- 2017
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 12:8
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Tidskriftsartikel (refereegranskat)abstract
- Accurately describing the anatomy of individual brains enables interlaboratory communication of functional and developmental studies and is crucial for possible surgical interventions. The human parietal lobe participates in multimodal sensory integration including language processing and also contains the primary somatosensory area. We describe detailed protocols to subdivide the parietal lobe, analyze morphological and volumetric characteristics, and create probabilistic atlases in MNI152 stereotaxic space. The parietal lobe was manually delineated on 3D T1 MR images of 30 healthy subjects and divided into four regions: supramarginal gyrus (SMG), angular gyrus (AG), superior parietal lobe (supPL) and postcentral gyrus (postCG). There was the expected correlation of male gender with larger brain and intracranial volume. We examined a wide range of anatomical features of the gyri and the sulci separating them. At least a rudimentary primary intermediate sulcus of Jensen (PISJ) separating SMG and AG was identified in nearly all (59/60) hemispheres. Presence of additional gyri in SMG and AG was related to sulcal features and volumetric characteristics. The parietal lobe was slightly (2%) larger on the left, driven by leftward asymmetries of the postCG and SMG. Intersubject variability was highest for SMG and AG, and lowest for postCG. Overall the morphological characteristics tended to be symmetrical, and volumes also tended to covary between hemispheres. This may reflect developmental as well as maturation factors. To assess the accuracy with which the labels can be used to segment newly acquired (unlabelled) T1-weighted brain images, we applied multi-atlas label propagation software (MAPER) in a leave-one-out experiment and compared the resulting automatic labels with the manually prepared ones. The results showed strong agreement (mean Jaccard index 0.69, corresponding to a mean Dice index of 0.82, average mean volume error of 0.6%). Stereotaxic probabilistic atlases of each subregion were obtained. They illustrate the physiological brain torque, with structures in the right hemisphere positioned more anteriorly than in the left, and right/left positional differences of up to 10 mm. They also allow an assessment of sulcal variability, e.g. low variability for parietooccipital fissure and cingulate sulcus. Illustrated protocols, individual label sets, probabilistic atlases, and a maximum-probability atlas which takes into account surrounding structures are available for free download under academic licences.
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| 10. |
- Yaakub, S. N., et al.
(författare)
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On brain atlas choice and automatic segmentation methods: a comparison of MAPER & FreeSurfer using three atlas databases
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Several automatic image segmentation methods and few atlas databases exist for analysing structural T1-weighted magnetic resonance brain images. The impact of choosing a combination has not hitherto been described but may bias comparisons across studies. We evaluated two segmentation methods (MAPER and FreeSurfer), using three publicly available atlas databases (Hammers_mith, Desikan-Killiany-Tourville, and MICCAI 2012 Grand Challenge). For each combination of atlas and method, we conducted a leave-one-out cross-comparison to estimate the segmentation accuracy of FreeSurfer and MAPER. We also used each possible combination to segment two datasets of patients with known structural abnormalities (Alzheimer’s disease (AD) and mesial temporal lobe epilepsy with hippocampal sclerosis (HS)) and their matched healthy controls. MAPER was better than FreeSurfer at modelling manual segmentations in the healthy control leave-one-out analyses in two of the three atlas databases, and the Hammers_mith atlas database transferred to new datasets best regardless of segmentation method. Both segmentation methods reliably identified known abnormalities in each patient group. Better separation was seen for FreeSurfer in the AD and left-HS datasets, and for MAPER in the right-HS dataset. We provide detailed quantitative comparisons for multiple anatomical regions, thus enabling researchers to make evidence-based decisions on their choice of atlas and segmentation method. © 2020, The Author(s).
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| 11. |
- Andersson, Karin, 1972, et al.
(författare)
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Inflammation in the hippocampus affects IGF1 receptor signaling and contributes to neurological sequelae in rheumatoid arthritis.
- 2018
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 1091-6490. ; 115:51
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Tidskriftsartikel (refereegranskat)abstract
- Rheumatoid arthritis (RA) is an inflammatory joint disease with a neurological component including depression, cognitive deficits, and pain, which substantially affect patients' quality of daily life. Insulin-like growth factor 1 receptor (IGF1R) signaling is one of the factors in RA pathogenesis as well as a known regulator of adult neurogenesis. The purpose of this study was to investigate the association between IGF1R signaling and the neurological symptoms in RA. In experimental RA, we demonstrated that arthritis induced enrichment of IBA1+ microglia in the hippocampus. This coincided with inhibitory phosphorylation of insulin receptor substrate 1 (IRS1) and up-regulation of IGF1R in the pyramidal cell layer of the cornus ammoni and in the dentate gyrus, reproducing the molecular features of the IGF1/insulin resistance. The aberrant IGF1R signaling was associated with reduced hippocampal neurogenesis, smaller hippocampus, and increased immobility of RA mice. Inhibition of IGF1R in experimental RA led to a reduction of IRS1 inhibition and partial improvement of neurogenesis. Evaluation of physical functioning and brain imaging in RA patients revealed that enhanced functional disability is linked with smaller hippocampus volume and aberrant IGF1R/IRS1 signaling. These results point to abnormal IGF1R signaling in the brain as a mediator of neurological sequelae in RA and provide support for the potentially reversible nature of hippocampal changes.
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| 12. |
- Bergquist, Magnus, 1960-, et al.
(författare)
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Trust and stakeholder perspectives on the implementation of AI tools in clinical radiology
- 2024
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Ingår i: European Radiology. - Heidelberg : Springer. - 0938-7994 .- 1432-1084. ; 34:1, s. 338-347
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: To define requirements that condition trust in artificial intelligence (AI) as clinical decision support in radiology from the perspective of various stakeholders and to explore ways to fulfil these requirements.Methods: Semi-structured interviews were conducted with twenty-five respondents—nineteen directly involved in the development, implementation, or use of AI applications in radiology and six working with AI in other areas of healthcare. We designed the questions to explore three themes: development and use of AI, professional decision-making, and management and organizational procedures connected to AI. The transcribed interviews were analysed in an iterative coding process from open coding to theoretically informed thematic coding.Results: We identified four aspects of trust that relate to reliability, transparency, quality verification, and inter-organizational compatibility. These aspects fall under the categories of substantial and procedural requirements.Conclusions: Development of appropriate levels of trust in AI in healthcare is complex and encompasses multiple dimensions of requirements. Various stakeholders will have to be involved in developing AI solutions for healthcare and radiology to fulfil these requirements. Clinical relevance statement: For AI to achieve advances in radiology, it must be given the opportunity to support, rather than replace, human expertise. Support requires trust. Identification of aspects and conditions for trust allows developing AI implementation strategies that facilitate advancing the field.Key Points:• Dimensions of procedural and substantial demands that need to be fulfilled to foster appropriate levels of trust in AI in healthcare are conditioned on aspects related to reliability, transparency, quality verification, and inter-organizational compatibility. • Creating the conditions for trust to emerge requires the involvement of various stakeholders, who will have to compensate the problem’s inherent complexity by finding and promoting well-defined solutions. © 2023, The Author(s).
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| 13. |
- Faillenot, Isabelle, et al.
(författare)
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Macroanatomy and 3D probabilistic atlas of the human insula.
- 2017
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Ingår i: NeuroImage. - : Elsevier BV. - 1095-9572 .- 1053-8119. ; 150, s. 88-98
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Tidskriftsartikel (refereegranskat)abstract
- The human insula is implicated in numerous functions. More and more neuroimaging studies focus on this region, however no atlas offers a complete subdivision of the insula in a reference space. The aims of this study were to define a protocol to subdivide insula, to create probability maps in the MNI152 stereotaxic space, and to provide normative reference volume measurements for these subdivisions. Six regions were manually delineated bilaterally on 3D T1 MR images of 30 healthy subjects: the three short gyri, the anterior inferior cortex, and the two long gyri. The volume of the insular grey matter was 7.7 ± 0.9cm(3) in native space and 9.9 ± 0.6cm(3) in MNI152 space. These volumes expressed as a percentage of the ipsilateral grey matter volume were minimally larger in women (2.7±0.2%) than in men (2.6±0.2%). After spatial normalization, a stereotactic probabilistic atlas of each subregion was produced, as well as a maximum-probability atlas taking into account surrounding structures. Automatically labelling insular subregions via a multi-atlas propagation and label fusion strategy (MAPER) in a leave-one-out experiment showed high spatial overlaps of such automatically defined insular subregions with the manually derived ones (mean Jaccard index 0.65, corresponding to a mean Dice index of 0.79), with an average mean volume error of 2.6%. Probabilistic and maximum probability atlases and the original delineations are available on the web under free academic licences.
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| 14. |
- Gryska, Emilia, 1992, et al.
(författare)
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Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.
- 2021
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Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field.Scoping review.Three databases (PubMed, IEEE Xplore and Scopus) were searched with tailored queries. Studies were included based on predefined criteria. Emerging themes during consecutive title, abstract, methods and whole-text screening were identified. The full-text analysis focused on materials, preprocessing, performance evaluation and comparison.Out of 2990 unique articles identified through the search, 441 articles met the eligibility criteria, with an estimated growth rate of 10% per year. We present a general overview and trends in the field with regard to publication sources, segmentation principles used and types of lesions. Algorithms are predominantly evaluated by measuring the agreement of segmentation results with a trusted reference. Few articles describe measures of clinical validity.The observed reporting practices leave room for improvement with a view to studying replication, method comparison and clinical applicability. To promote this improvement, we propose a list of recommendations for future studies in the field.
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| 15. |
- Gryska, Emilia, 1992, et al.
(författare)
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Automatic brain lesion segmentation on standard MRIs of the human head: a scoping review protocol.
- 2019
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Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 9:2
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Tidskriftsartikel (refereegranskat)abstract
- Automatic brain lesion segmentation from medical images has great potential to support clinical decision making. Although numerous methods have been proposed, significant challenges must be addressed before they will become established in clinical and research practice. We aim to elucidate the state of the art, to provide a synopsis of competing approaches and identify contrasts between them.We present the background and study design of a scoping review for automatic brain lesion segmentation methods for conventional MRI according to the framework proposed by Arksey and O'Malley. We aim to identify common image processing steps as well as mathematical and computational theories implemented in these methods. We will aggregate the evidence on the efficacy and identify limitations of the approaches. Methods to be investigated work with standard MRI sequences from human patients examined for brain lesions, and are validated with quantitative measures against a trusted reference. PubMed, IEEE Xplore and Scopus will be searched using search phrases that will ensure an inclusive and unbiased overview. For matching records, titles and abstracts will be screened to ensure eligibility. Studies will be excluded if a full paper is not available or is not written in English, if non-standard MR sequences are used, if there is no quantitative validation, or if the method is not automatic. In the data charting phase, we will extract information about authors, publication details and study cohort. We expect to find information about preprocessing, segmentation and validation procedures. We will develop an analytical framework to collate, summarise and synthesise the data.Ethical approval for this study is not required since the information will be extracted from published studies. We will submit the review report to a peer-reviewed scientific journal and explore other venues for presenting the work.
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| 16. |
- Gryska, Emilia, 1992, et al.
(författare)
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Deep learning for automatic brain tumour segmentation on MRI: evaluation of recommended reporting criteria via a reproduction and replication study.
- 2022
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Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 12:7
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Tidskriftsartikel (refereegranskat)abstract
- To determine the reproducibility and replicability of studies that develop and validate segmentation methods for brain tumours on MRI and that follow established reproducibility criteria; and to evaluate whether the reporting guidelines are sufficient.Two eligible validation studies of distinct deep learning (DL) methods were identified. We implemented the methods using published information and retraced the reported validation steps. We evaluated to what extent the description of the methods enabled reproduction of the results. We further attempted to replicate reported findings on a clinical set of images acquired at our institute consisting of high-grade and low-grade glioma (HGG, LGG), and meningioma (MNG) cases.We successfully reproduced one of the two tumour segmentation methods. Insufficient description of the preprocessing pipeline and our inability to replicate the pipeline resulted in failure to reproduce the second method. The replication of the first method showed promising results in terms of Dice similarity coefficient (DSC) and sensitivity (Sen) on HGG cases (DSC=0.77, Sen=0.88) and LGG cases (DSC=0.73, Sen=0.83), however, poorer performance was observed for MNG cases (DSC=0.61, Sen=0.71). Preprocessing errors were identified that contributed to low quantitative scores in some cases.Established reproducibility criteria do not sufficiently emphasise description of the preprocessing pipeline. Discrepancies in preprocessing as a result of insufficient reporting are likely to influence segmentation outcomes and hinder clinical utilisation. A detailed description of the whole processing chain, including preprocessing, is thus necessary to obtain stronger evidence of the generalisability of DL-based brain tumour segmentation methods and to facilitate translation of the methods into clinical practice.
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| 17. |
- Hellström, William, et al.
(författare)
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Postnatal serum IGF-1 levels associate with brain volumes at term in extremely preterm infants
- 2023
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Ingår i: Pediatric Research. - : Springer Science and Business Media LLC. - 0031-3998 .- 1530-0447. ; 93:3, s. 666-674
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Tidskriftsartikel (refereegranskat)abstract
- Background Growth factors important for normal brain development are low in preterm infants. This study investigated the link between growth factors and preterm brain volumes at term. Material/methods Infants born <28 weeks gestational age (GA) were included. Endogenous levels of insulin-like growth factor (IGF)-1, brain-derived growth factor, vascular endothelial growth factor, and platelet-derived growth factor (expressed as area under the curve [AUC] for serum samples from postnatal days 1, 7, 14, and 28) were utilized in a multivariable linear regression model. Brain volumes were determined by magnetic resonance imaging (MRI) at term equivalent age. Results In total, 49 infants (median [range] GA 25.4 [22.9-27.9] weeks) were included following MRI segmentation quality assessment and AUC calculation. IGF-1 levels were independently positively associated with the total brain (p < 0.001, beta = 0.90), white matter (p = 0.007, beta = 0.33), cortical gray matter (p = 0.002, beta = 0.43), deep gray matter (p = 0.008, beta = 0.05), and cerebellar (p = 0.006, beta = 0.08) volume adjusted for GA at birth and postmenstrual age at MRI. No associations were seen for other growth factors. Conclusions Endogenous exposure to IGF-1 during the first 4 weeks of life was associated with total and regional brain volumes at term. Optimizing levels of IGF-1 might improve brain growth in extremely preterm infants. Impact High serum levels of insulin-like growth factor (IGF)-1 during the first month of life were independently associated with increased total brain volume, white matter, gray matter, and cerebellar volume at term equivalent age in extremely preterm infants. IGF-1 is a critical regulator of neurodevelopment and postnatal levels are low in preterm infants. The effects of IGF-1 levels on brain development in extremely preterm infants are not fully understood. Optimizing levels of IGF-1 may benefit early brain growth in extremely preterm infants. The effects of systemically administered IGF-1/IGFBP3 in extremely preterm infants are now being investigated in a randomized controlled trial (Clinicaltrials.gov: NCT03253263).
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| 18. |
- Holmberg, Mats, 1958, et al.
(författare)
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A Longitudinal Study of Medial Temporal Lobe Volumes in Graves Disease
- 2022
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Ingår i: Journal of Clinical Endocrinology and Metabolism. - : The Endocrine Society. - 0021-972X .- 1945-7197. ; 107:4, s. 1040-1052
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Tidskriftsartikel (refereegranskat)abstract
- Context: Neuropsychiatric symptoms are common features of Graves disease (GD) in hyperthyroidism and after treatment. The mechanism behind these symptoms is unknown, but reduced hippocampal volumes have been observed in association with increased thyroid hormone levels. Objective: This work aimed at investigating GD influence on regional medial temporal lobe (MTL) volumes. Methods: Sixty-two women with newly diagnosed GD underwent assessment including magnetic resonance (MR) imaging in hyperthyroidism and 48 of them were followed up after a mean of 16.4±4.2 SD months of treatment. Matched thyroid-healthy controls were also assessed twice at a 15-month interval. MR images were automatically segmented using multiatlas propagation with enhanced registration. Regional medial temporal lobe (MTL) volumes for amygdalae and hippocampi were compared with clinical data and data from symptom questionnaires and neuropsychological tests. Results: Patients had smaller MTL regions than controls at inclusion. At follow-up, all 4 MTL regions had increased volumes and only the volume of the left amygdala remained reduced compared to controls. There were significant correlations between the level of thyrotropin receptor antibodies (TRAb) and MTL volumes at inclusion and also between the longitudinal difference in the levels of free 3,5,3′-triiodothyronine and TRAb and the difference in MTL volumes. There were no significant correlations between symptoms or test scores and any of the 4 MTL volumes. Conclusion: Dynamic alterations in the amygdalae and hippocampi in GD reflect a previously unknown level of brain involvement both in the hyperthyroid state of the condition and after treatment. The clinical significance, as well as the mechanisms behind these novel findings, warrant further study of the neurological consequences of GD.
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| 19. |
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| 20. |
- Holmberg, Mats, 1958, et al.
(författare)
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Structural brain changes in hyperthyroid Graves' disease: protocol for an ongoing longitudinal, case-controlled study in Göteborg, Sweden-the CogThy project.
- 2019
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Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 9:11
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Tidskriftsartikel (refereegranskat)abstract
- Cognitive impairment and reduced well-being are common manifestations of Graves' disease (GD). These symptoms are not only prevalent during the active phase of the disease but also often prevail for a long time after hyperthyroidism is considered cured. The pathogenic mechanisms involved in these brain-derived symptoms are currently unknown. The overall aim of the CogThy study is to identify the mechanism behind cognitive impairment to be able to recognise GD patients at risk.The study is a longitudinal, single-centre, case-controlled study conducted in Göteborg, Sweden on premenopausal women with newly diagnosed GD. The subjects are examined: at referral, at inclusion and then every 3.25 months until 15 months. Examinations include: laboratory measurements; eye evaluation; neuropsychiatric and neuropsychological testing; structural MRI of the whole brain, orbits and medial temporal lobe structures; functional near-infrared spectroscopy of the cerebral prefrontal cortex and self-assessed quality of life questionnaires. The primary outcome measure is the change in medial temporal lobe structure volume. Secondary outcome measures include neuropsychological, neuropsychiatric, hormonal and autoantibody variables. The study opened for inclusion in September 2012 and close for inclusion in October 2019. It will provide novel information on the effect of GD on medial temporal lobe structures and cerebral cortex functionality as well as whether these changes are associated with cognitive and affective impairment, hormonal levels and/or autoantibody levels. It should lead to a broader understanding of the underlying pathogenesis and future treatment perspectives.The study has been reviewed and approved by the Regional Ethical Review Board in Göteborg, Sweden. The results will be actively disseminated through peer-reviewed journals, national and international conference presentations and among patient organisations after an appropriate embargo time.44321 at the public project database for research and development in Västra Götaland County, Sweden (https://www.researchweb.org/is/vgr/project/44321).
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| 21. |
- Hortensius, Lisa M., et al.
(författare)
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Serum docosahexaenoic acid levels are associated with brain volumes in extremely preterm born infants
- 2021
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Ingår i: Pediatric Research. - : Springer Science and Business Media LLC. - 0031-3998 .- 1530-0447. ; 90:6, s. 1177-1185
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Tidskriftsartikel (refereegranskat)abstract
- Background: Docosahexaenoic acid (DHA) and arachidonic acid (AA) are important for fetal brain growth and development. Our aim was to evaluate the association between serum DHA and AA levels and brain volumes in extremely preterm infants. Methods: Infants born at <28 weeks gestational age in 2013–2015, a cohort derived from a randomized controlled trial comparing two types of parenteral lipid emulsions, were included (n = 90). Serum DHA and AA levels were measured at postnatal days 1, 7, 14, and 28, and the area under the curve was calculated. Magnetic resonance (MR) imaging was performed at term-equivalent age (n = 66), and volumes of six brain regions were automatically generated. Results: After MR image quality assessment and area under the curve calculation, 48 infants were included (gestational age mean [SD] 25.5 [1.4] weeks). DHA levels were positively associated with total brain (B = 7.966, p = 0.012), cortical gray matter (B = 3.653, p = 0.036), deep gray matter (B = 0.439, p = 0.014), cerebellar (B = 0.932, p = 0.003), and white matter volume (B = 3.373, p = 0.022). AA levels showed no association with brain volumes. Conclusions: Serum DHA levels during the first 28 postnatal days were positively associated with volumes of several brain structures in extremely preterm infants at term-equivalent age. Impact: Higher serum levels of DHA in the first 28 postnatal days are positively associated with brain volumes at term-equivalent age in extremely preterm born infants.Especially the most immature infants suffer from low DHA levels in the first 28 postnatal days, with little increase over time.Future research is needed to explore whether postnatal fatty acid supplementation can improve brain development and may serve as a nutritional preventive and therapeutic treatment option in extremely preterm infants.
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| 22. |
- Klein-Koerkamp, Yanica, et al.
(författare)
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Amygdalar atrophy in early Alzheimer's disease
- 2014
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Ingår i: Current Alzheimer Research. - : Bentham Science Publishers Ltd.. - 1567-2050 .- 1875-5828. ; 11:3, s. 239-252
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Tidskriftsartikel (refereegranskat)abstract
- Current research suggests that amygdalar volumes in patients with Alzheimer's disease (AD) may be a relevant measure for its early diagnosis. However, findings are still inconclusive and controversial, partly because studies did not focus on the earliest stage of the disease. In this study, we measured amygdalar atrophy in 48 AD patients and 82 healthy controls (HC) by using a multi-atlas procedure, MAPER. Both hippocampal and amygdalar volumes, normalized by intracranial volume, were significantly reduced in AD compared with HC. The volume loss in the two structures was of similar magnitude (~24%). Amygdalar volume loss in AD predicted memory impairment after we controlled for age, gender, education, and, more important, hippocampal volume, indicating that memory decline correlates with amygdalar atrophy over and above hippocampal atrophy. Amygdalar volume may thus be as useful as hippocampal volume for the diagnosis of early AD. In addition, it could be an independent marker of cognitive decline. The role of the amygdala in AD should be reconsidered to guide further research and clinical practice. © 2014 Bentham Science Publishers.
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| 23. |
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| 24. |
- Rados, Matea, et al.
(författare)
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Diagnostic value of MRI in the presurgical evaluation of patients with epilepsy: influence of field strength and sequence selection: a systematic review and meta-analysis from the E-PILEPSY Consortium.
- 2022
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Ingår i: Epileptic disorders. - : Wiley. - 1950-6945 .- 1294-9361. ; 24:2, s. 323-342
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Tidskriftsartikel (refereegranskat)abstract
- MRI is a cornerstone in presurgical evaluation of epilepsy. Despite guidelines, clinical practice varies. In light of the E-PILEPSY pilot reference network, we conducted a systematic review and meta-analysis on the diagnostic value of MRI in the presurgical evaluation of epilepsy patients. We included original research articles on diagnostic value of higher MRI field strength and guideline-recommended and additional MRI sequences in detecting an epileptogenic lesion in adult or paediatric epilepsy surgery candidates. Lesion detection rate was used as a metric in meta-analysis. Eighteen studies were included for MRI field strength and 25 for MRI sequences, none were free from bias. In patients with normal MRI at lower-field strength, 3T improved lesion detection rate by 18% and 7T by 23%. Field strengths higher than 1.5T did not have higher lesion detection rates in patients with hippocampal sclerosis (HS). The lesion detection rate of epilepsy-specific MRI protocols was 83% for temporal lobe epilepsy (TLE) patients. Dedicated MRI protocols and evaluation by an experienced epilepsy neuroradiologist increased lesion detection. For HS, 3DT1, T2, and FLAIR each had a lesion detection rate at around 90%. Apparent diffusion coefficient indices had a lateralizing value of 33% for TLE. DTI fractional anisotropy and mean diffusivity had a localizing value of 8% and 34%. A dedicated MRI protocol and expert evaluation benefits lesion detection rate in epilepsy surgery candidates. If patients remain MRI negative, imaging at higher-field strength may reveal lesions. In HS, apparent diffusion coefficient indices may aid lateralization and localization more than increasing field strength. DTI can add further diagnostic information. For other additional sequences, the quality and number of studies is insufficient to draw solid conclusions. Our findings may be used as evidence base for developing new high-quality MRI studies and clinical guidelines.
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| 25. |
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| 26. |
- Sapey-Triomphe, Laurie-Anne, et al.
(författare)
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Neuroanatomical Correlates of Recognizing Face Expressions in Mild Stages of Alzheimer's Disease.
- 2015
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Ingår i: PloS one. - : Public Library of Science (PLoS). - 1932-6203. ; 10:12
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Tidskriftsartikel (refereegranskat)abstract
- Early Alzheimer's disease can involve social disinvestment, possibly as a consequence of impairment of nonverbal communication skills. This study explores whether patients with Alzheimer's disease at the mild cognitive impairment or mild dementia stage have impaired recognition of emotions in facial expressions, and describes neuroanatomical correlates of emotion processing impairment. As part of the ongoing PACO study (personality, Alzheimer's disease and behaviour), 39 patients with Alzheimer's disease at the mild cognitive impairment or mild dementia stage and 39 matched controls completed tests involving discrimination of four basic emotions-happiness, fear, anger, and disgust-on photographs of faces. In patients, automatic volumetry of 83 brain regions was performed on structural magnetic resonance images using MAPER (multi-atlas propagation with enhanced registration). From the literature, we identified for each of the four basic emotions one brain region thought to be primarily associated with the function of recognizing that emotion. We hypothesized that the volume of each of these regions would be correlated with subjects' performance in recognizing the associated emotion. Patients showed deficits of basic emotion recognition, and these impairments were correlated with the volumes of the expected regions of interest. Unexpectedly, most of these correlations were negative: better emotional facial recognition was associated with lower brain volume. In particular, recognition of fear was negatively correlated with the volume of amygdala, disgust with pallidum, and happiness with fusiform gyrus. Recognition impairment in mild stages of Alzheimer's disease for a given emotion was thus associated with less visible atrophy of functionally responsible brain structures within the patient group. Possible explanations for this counterintuitive result include neuroinflammation, regional β-amyloid deposition, or transient overcompensation during early stages of Alzheimer's disease.
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| 27. |
- Sousa, João M., et al.
(författare)
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Accuracy and precision of zero-echo-time, single- and multi-atlas attenuation correction for dynamic [11C]PE2I PET-MR brain imaging
- 2020
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Ingår i: EJNMMI Physics. - : Springer Science and Business Media LLC. - 2197-7364. ; 7:1
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: A valid photon attenuation correction (AC) method is instrumental for obtaining quantitatively correct PET images. Integrated PET/MR systems provide no direct information on attenuation, and novel methods for MR-based AC (MRAC) are still under investigation. Evaluations of various AC methods have mainly focused on static brain PET acquisitions. In this study, we determined the validity of three MRAC methods in a dynamic PET/MR study of the brain.METHODS: Nine participants underwent dynamic brain PET/MR scanning using the dopamine transporter radioligand [11C]PE2I. Three MRAC methods were evaluated: single-atlas (Atlas), multi-atlas (MaxProb) and zero-echo-time (ZTE). The 68Ge-transmission data from a previous stand-alone PET scan was used as reference method. Parametric relative delivery (R1) images and binding potential (BPND) maps were generated using cerebellar grey matter as reference region. Evaluation was based on bias in MRAC maps, accuracy and precision of [11C]PE2I BPND and R1 estimates, and [11C]PE2I time-activity curves. BPND was examined for striatal regions and R1 in clusters of regions across the brain.RESULTS: For BPND, ZTE-MRAC showed the highest accuracy (bias < 2%) in striatal regions. Atlas-MRAC exhibited a significant bias in caudate nucleus (- 12%) while MaxProb-MRAC revealed a substantial, non-significant bias in the putamen (9%). R1 estimates had a marginal bias for all MRAC methods (- 1.0-3.2%). MaxProb-MRAC showed the largest intersubject variability for both R1 and BPND. Standardized uptake values (SUV) of striatal regions displayed the strongest average bias for ZTE-MRAC (~ 10%), although constant over time and with the smallest intersubject variability. Atlas-MRAC had highest variation in bias over time (+10 to - 10%), followed by MaxProb-MRAC (+5 to - 5%), but MaxProb showed the lowest mean bias. For the cerebellum, MaxProb-MRAC showed the highest variability while bias was constant over time for Atlas- and ZTE-MRAC.CONCLUSIONS: Both Maxprob- and ZTE-MRAC performed better than Atlas-MRAC when using a 68Ge transmission scan as reference method. Overall, ZTE-MRAC showed the highest precision and accuracy in outcome parameters of dynamic [11C]PE2I PET analysis with use of kinetic modelling.
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| 28. |
- Srikrishna, Meera, et al.
(författare)
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Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT
- 2022
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Ingår i: Frontiers in Computational Neuroscience. - : Frontiers Media SA. - 1662-5188. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Brain tissue segmentation plays a crucial role in feature extraction, volumetric quantification, and morphometric analysis of brain scans. For the assessment of brain structure and integrity, CT is a non-invasive, cheaper, faster, and more widely available modality than MRI. However, the clinical application of CT is mostly limited to the visual assessment of brain integrity and exclusion of copathologies. We have previously developed two-dimensional (2D) deep learning-based segmentation networks that successfully classified brain tissue in head CT. Recently, deep learning-based MRI segmentation models successfully use patch-based three-dimensional (3D) segmentation networks. In this study, we aimed to develop patch-based 3D segmentation networks for CT brain tissue classification. Furthermore, we aimed to compare the performance of 2D- and 3D-based segmentation networks to perform brain tissue classification in anisotropic CT scans. For this purpose, we developed 2D and 3D U-Net-based deep learning models that were trained and validated on MR-derived segmentations from scans of 744 participants of the Gothenburg H70 Cohort with both CT and T1-weighted MRI scans acquired timely close to each other. Segmentation performance of both 2D and 3D models was evaluated on 234 unseen datasets using measures of distance, spatial similarity, and tissue volume. Single-task slice-wise processed 2D U-Nets performed better than multitask patch-based 3D U-Nets in CT brain tissue classification. These findings provide support to the use of 2D U-Nets to segment brain tissue in one-dimensional (1D) CT. This could increase the application of CT to detect brain abnormalities in clinical settings.
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| 29. |
- Srikrishna, Meera, et al.
(författare)
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CT-based volumetric measures obtained through deep learning: Association with biomarkers of neurodegeneration
- 2024
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Ingår i: Alzheimers & Dementia. - 1552-5260. ; 20:1, s. 629-640
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Tidskriftsartikel (refereegranskat)abstract
- INTRODUCTIONCranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep-learning-based model that produced accurate and robust cranial CT tissue classification.MATERIALS AND METHODSWe analyzed 917 CT and 744 magnetic resonance (MR) scans from the Gothenburg H70 Birth Cohort, and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore. We tested associations between six CT-based volumetric measures (CTVMs) and existing clinical diagnoses, fluid and imaging biomarkers, and measures of cognition.RESULTSCTVMs differentiated cognitively healthy individuals from dementia and prodromal dementia patients with high accuracy levels comparable to MR-based measures. CTVMs were significantly associated with measures of cognition and biochemical markers of neurodegeneration.DISCUSSIONThese findings suggest the potential future use of CT-based volumetric measures as an informative first-line examination tool for neurodegenerative disease diagnostics after further validation.HIGHLIGHTSComputed tomography (CT)-based volumetric measures can distinguish between patients with neurodegenerative disease and healthy controls, as well as between patients with prodromal dementia and controls.CT-based volumetric measures associate well with relevant cognitive, biochemical, and neuroimaging markers of neurodegenerative diseases.Model performance, in terms of brain tissue classification, was consistent across two cohorts of diverse nature.Intermodality agreement between our automated CT-based and established magnetic resonance (MR)-based image segmentations was stronger than the agreement between visual CT and MR imaging assessment.
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| 30. |
- Srikrishna, Meera, et al.
(författare)
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Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT
- 2021
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 244
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Tidskriftsartikel (refereegranskat)abstract
- Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more widely available than MR scans, their application is currently limited to the visual assessment of brain integrity and the exclusion of co-pathologies. CT has rarely been used for tissue classification because the contrast between grey matter and white matter was considered insufficient. In this study, we propose an automatic method for segmenting grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume (ICV) from head CT images. A U-Net deep learning model was trained and validated on CT images with MRI-derived segmentation labels. We used data from 744 participants of the Gothenburg H70 Birth Cohort Studies for whom CT and T1-weighted MR images had been acquired on the same day. Our proposed model predicted brain tissue classes accurately from unseen CT images (Dice coefficients of 0.79, 0.82, 0.75, 0.93 and 0.98 for GM, WM, CSF, brain volume and ICV, respectively). To contextualize these results, we generated benchmarks based on established MR-based methods and intentional image degradation. Our findings demonstrate that CT-derived segmentations can be used to delineate and quantify brain tissues, opening new possibilities for the use of CT in clinical practice and research.
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| 31. |
- Steinbart, David, et al.
(författare)
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Automatic and manual segmentation of the piriform cortex: Method development and validation in patients with temporal lobe epilepsy and Alzheimer's disease.
- 2023
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Ingår i: Human brain mapping. - 1065-9471 .- 1097-0193. ; 44:8, s. 3196-3209
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Tidskriftsartikel (refereegranskat)abstract
- The piriform cortex (PC) is located at the junction of the temporal and frontal lobes. It is involved physiologically in olfaction as well as memory and plays an important role in epilepsy. Its study at scale is held back by the absence of automatic segmentation methods on MRI. We devised a manual segmentation protocol for PC volumes, integrated those manually derived images into the Hammers Atlas Database (n=30) and used an extensively validated method (multi-atlas propagation with enhanced registration, MAPER) for automatic PC segmentation. We applied automated PC volumetry to patients with unilateral temporal lobe epilepsy with hippocampal sclerosis (TLE; n=174 including n=58 controls) and to the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI; n=151, of whom with mild cognitive impairment (MCI), n=71; Alzheimer's disease (AD), n=33; controls, n=47). In controls, mean PC volume was 485mm3 on the right and 461mm3 on the left. Automatic and manual segmentations overlapped with a Jaccard coefficient (intersection/union) of ~0.5 and a mean absolute volume difference of ~22mm3 in healthy controls, ~0.40/ ~28mm3 in patients with TLE, and~0.34/~29mm3 in patients with AD. In patients with TLE, PC atrophy lateralised to the side of hippocampal sclerosis (p<.001). In patients with MCI and AD, PC volumes were lower than those of controls bilaterally (p<.001). Overall, we have validated automatic PC volumetry in healthy controls and two types of pathology. The novel finding of early atrophy of PC at the stage of MCI possibly adds a novel biomarker. PC volumetry can now be applied at scale.
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| 32. |
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